The natural ecological environment serves as the cornerstone of human survival and development. However, with the advancement of human society, environmental pollution has emerged as a prominent global issue. Among these, air pollution, a critical component of environmental degradation, exerts profound impacts on human health, ecological balance, and even the global economy. To effectively address air pollution, nations have intensified their control efforts, with environmental monitoring—playing an increasingly pivotal role. This study focuses on the application of environmental monitoring in air pollution control, delving into specific methodologies such as real-time atmospheric spectral analysis, sensor network deployment for PM2.5 tracking, and big data-driven pollution source apportionment models. By integrating these technical approaches, the research illuminates how multi-source data fusion and intelligent analysis in environmental monitoring enhance the precision of pollution trend prediction and the effectiveness of targeted control strategies, thereby underscoring its contribution to optimizing air quality management frameworks.